TimeSieve: Extracting Temporal Dynamics through Information Bottlenecks
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914918446399488 |
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| author | Feng, Ninghui Lai, Songning Yang, Jiayu Zhou, Fobao Yin, Zhenxiao Zhao, Hang |
| author_facet | Feng, Ninghui Lai, Songning Yang, Jiayu Zhou, Fobao Yin, Zhenxiao Zhao, Hang |
| contents | Time series forecasting has become an increasingly popular research area due to its critical applications in various real-world domains such as traffic management, weather prediction, and financial analysis. Despite significant advancements, existing models face notable challenges, including the necessity of manual hyperparameter tuning for different datasets, and difficulty in effectively distinguishing signal from redundant features in data characterized by strong seasonality. These issues hinder the generalization and practical application of time series forecasting models. To solve this issues, we propose an innovative time series forecasting model TimeSieve designed to address these challenges. Our approach employs wavelet transforms to preprocess time series data, effectively capturing multi-scale features without the need for additional parameters or manual hyperparameter tuning. Additionally, we introduce the information bottleneck theory that filters out redundant features from both detail and approximation coefficients, retaining only the most predictive information. This combination reduces significantly improves the model's accuracy. Extensive experiments demonstrate that our model outperforms existing state-of-the-art methods on 70% of the datasets, achieving higher predictive accuracy and better generalization across diverse datasets. Our results validate the effectiveness of our approach in addressing the key challenges in time series forecasting, paving the way for more reliable and efficient predictive models in practical applications. The code for our model is available at https://github.com/xll0328/TimeSieve. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_05036 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TimeSieve: Extracting Temporal Dynamics through Information Bottlenecks Feng, Ninghui Lai, Songning Yang, Jiayu Zhou, Fobao Yin, Zhenxiao Zhao, Hang Machine Learning Artificial Intelligence Time series forecasting has become an increasingly popular research area due to its critical applications in various real-world domains such as traffic management, weather prediction, and financial analysis. Despite significant advancements, existing models face notable challenges, including the necessity of manual hyperparameter tuning for different datasets, and difficulty in effectively distinguishing signal from redundant features in data characterized by strong seasonality. These issues hinder the generalization and practical application of time series forecasting models. To solve this issues, we propose an innovative time series forecasting model TimeSieve designed to address these challenges. Our approach employs wavelet transforms to preprocess time series data, effectively capturing multi-scale features without the need for additional parameters or manual hyperparameter tuning. Additionally, we introduce the information bottleneck theory that filters out redundant features from both detail and approximation coefficients, retaining only the most predictive information. This combination reduces significantly improves the model's accuracy. Extensive experiments demonstrate that our model outperforms existing state-of-the-art methods on 70% of the datasets, achieving higher predictive accuracy and better generalization across diverse datasets. Our results validate the effectiveness of our approach in addressing the key challenges in time series forecasting, paving the way for more reliable and efficient predictive models in practical applications. The code for our model is available at https://github.com/xll0328/TimeSieve. |
| title | TimeSieve: Extracting Temporal Dynamics through Information Bottlenecks |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2406.05036 |